Decoding subject-driven cognitive states with whole-brain connectivity patterns
Open accessAbstract
Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.
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In between-group analysis, the left DLPFC displayed significantly enhanced connectivity to ipsilateral insular cortex and contralateral supramarginal gyrus in highs compared to lows during hypnosis compared to rest.
“In between-group analysis using functionally defined ROIs (Shirer et al. 2012) based upon our a priori hypotheses, the left DLPFC displayed significantly enhanced connectivity to ipsilateral insular cortex and contralateral supramarginal gyrus in highs compared to lows during hypnosis compared to rest (P < 0.05, corrected for multiple comparisons with FEAT's GRF correction; Fig. 3A; Table 3).”
From Brain Activity and Functional Connectivity Associated with Hypnosis (2016) · cites this paper as Shirer et al. 2012
Functionally defined regions of interest have recently been proposed for whole-brain connectivity analysis.
“Functionally defined regions of interest have recently been suggested in whole-brain connectivity analysis (Shirer et al., 2012).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Shirer et al., 2012
Using the 90 functional regions of interest defined by Shirer et al. (2012), the support vector machine classifier achieved a classification accuracy of 92.5%.
“Using the 90 functional regions of interest defined by Shirer et al. (2012), the support vector machine classifier performed with a classification accuracy of 92.5%.”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Shirer et al., 2012
This kind of analysis has previously been used to predict subjects' psychiatric condition and cognitive state.
“This kind of analysis has previously been used (albeit from full correlation network matrices) to predict subjects’ psychiatric condition [77] and cognitive state [78];”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [78]
The Stanford functional ROI atlas consists of 14 individual ICNs, including Anterior- and Posterior Salience, Auditory, Basal Ganglia, Dorsal- and Ventral Default Mode, Higher Visual, Language, Left- and Right Executive Control, Sensorimotor, Precuneus, Primary Visual and Visuospatial networks.
“To provide a framework for such a network classification, we used the Stanford functional ROI atlas (http://findlab.stanford.edu/func- tional_ROIs.html),consisting of 14 individual ICNs, including Anterior-and Posterior Salience, Auditory, Basal Ganglia, Dorsal-and Ventral Default Mode, Higher Visual, Language, Left-and Right Executive Control, Sensorimotor, Precuneus, Primary Visual and Visuosp”
From Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks (2015) · cites this paper as Shirer et al., 2012
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- Authors
- Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-05-26
- Volume
- 22
- Issue
- 1
- Pages
- 158-165
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC3236795
- OpenAlex
- W2101219946
- Cited by
- 1,843
- References
- 59
- Citations by year
- 2026: 59 · 2025: 98 · 2024: 111 · 2023: 126 · 2022: 173 · 2021: 207 · 2020: 161 · 2019: 174 · 2018: 158 · 2017: 161 · 2016: 131 · 2015: 113 · 2014: 76 · 2013: 55 · 2012: 32
- FWCI
- 27.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS073498, NS058899, R21NS058899, NS073498)
- National Center for Complementary and Integrative Health (AT005733, RC1AT005733)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057610, HD057610, HD059205, R01HD059205)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- cognitive state decoding, whole-brain functional connectivity, resting-state brain networks, brain state classification, memory retrieval
- MeSH terms
- Adolescent, Adult, Brain, Brain Mapping, Cognition, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Neuropsychological Tests, Oxygen, Mental Recall, Cohort Studies, Young Adult
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